Big Tech Stock Prices

An analysis of 14 Big Tech stocks from 2010 - 2020

Author

Tech Titans

Abstract

The goal of this project is to understand the temporal behavior of Big Tech stocks that are actively traded on the NYSE. This analysis will show trends in the market to understand times when the market was on a downtrend (bear market) or in an uptrend (bull market). This will provide insight into times when investments should be made or when they should be pulled back. Investment analyses such as the aformentioned are extremely valuable to large finanacial institutions that aim to benefit from growth in the market. This is extremely important for those who place their hard earned dollar with these institutions as a means to save for retirement.

The analysis will consist of answering two key questions in a sequential manner: exploratory data analysis (EDA), data wrangling, and data visualization. This process is key to gaining insight from the data and allows for a clean platform that can used to train machine learning models.

Introduction to the Dataset

The dataset used in this analysis was sourced from Tidy Tuesday; an open source conglomeration of data for visualization and machine learning. The dataset contains information on 14 different Big Tech companies’s stock price and trading volume from 2010 to 2022 on the New York Stock Exchange. There are two .csv (comma seperated variable) files used, one called “big_tech_stock_prices.csv” and another called “big_tech_companies.csv”. For simplicity, the files are renamed “stocks” and “companies”, respectively. Stocks contains a majority of the information used in this analysis, including the daily low, high, open, and close prices, as well as trading volume. Low price represents the lowest price for the day while high price represents the opposite. Open price represents the price when the market opens at 9:30am ET, and close price represents the price when the market closes at 4pm ET. Trading volume represents the amount of stocks bought and sold that day; this metric is important for understanding the popularity of the stock.

The interest in this dataset was sparked by its potential to provide valuable insights into the stock market behavior of big tech companies. This enables a comprehensive examination of stock market trends, facilitates comparisons across various companies, and aids in assessing the influence of significant external events on stock valuations.

Q1: How do stock prices change over time based on basic economic metrics

Introduction

To answer the question of how stock prices change over time, the daily price movement and volume will be visualized. Looking at price changes temporally will provide insights into past trends in the stock market. The stock market is cyclical, and thus finding patterns in the past data will allow for insights into future movements of the stock market. This is benefical because it can lead to knowledge of when a stock price will rise. The interest in this question is based on team interest in investing and making low-risk financial decisions.

Summary Statistics:
                    open                                                 \
                    mean      median        min         max         std   
stock_symbol                                                              
AAPL           51.274171   29.745001   6.870357  182.630005   47.316509   
ADBE          186.023938   97.589996  22.969999  696.280029  173.562115   
AMZN           58.937204   36.325001   5.296500  187.199997   54.138607   
CRM           103.419948   76.290001  15.522500  310.000000   71.287894   
CSCO           33.493880   29.500000  13.930000   64.040001   12.643887   
GOOGL          49.149625   38.521000  10.968719  151.250000   35.809816   
IBM           148.427471  143.173996  90.439774  205.908218   23.998357   
INTC           36.481406   34.270000  17.879999   68.199997   12.912534   
META          147.874275  141.620002  18.080000  381.679993   86.734656   
MSFT          100.048490   55.660000  23.090000  344.619995   88.228008   
NFLX          188.242206  110.010002   6.960000  692.349976  178.967463   
NVDA           50.560335   11.902500   2.180000  335.170013   69.538684   
ORCL           46.242177   41.750000  21.459999  104.290001   16.864193   
TSLA           58.859467   16.229000   1.076000  411.470001   95.677282   

                    volume                                                   
                      mean       median       min         max           std  
stock_symbol                                                                 
AAPL          2.563255e+08  166674000.0  35195900  1880998000  2.225768e+08  
ADBE          3.814337e+06    2948500.0    589200   108752400  3.598144e+06  
AMZN          8.833999e+07   74592000.0  17626000   848422000  5.309249e+07  
CRM           6.910973e+06    5548800.0   1084700    64562800  5.048860e+06  
CSCO          3.269656e+07   25482400.0   5720500   560040200  2.570963e+07  
GOOGL         6.018647e+07   41234000.0   9312000   592399008  4.957963e+07  
IBM           5.036545e+06    4345189.0   1247878    39814421  2.772073e+06  
INTC          3.607170e+07   29874600.0   5893800   199002600  2.123178e+07  
META          3.117815e+07   23239000.0   5913100   573576400  2.713267e+07  
MSFT          3.801647e+07   32280800.0   7425600   319317900  2.147328e+07  
NFLX          1.841485e+07   11961800.0   1144000   315541800  2.054316e+07  
NVDA          5.080613e+07   43395600.0   4564400   369292800  3.210953e+07  
ORCL          1.801856e+07   14699800.0   2754900   183503900  1.251053e+07  
TSLA          9.351647e+07   75914250.0   1777500   914082000  8.164780e+07  

Approach

To begin the analysis of question one, the data is first summarized for basic statistics such as mean, median, standard deviation for both price variation and trading volume. This allows for a basic understanding of the distribution of the data and how metrics such as outliers influence the spread of the data. Once a basic understanding of the data is formed, the data is then visualized.

One step that is typically used for raw, uncleaned data is filtering. This allows for the data to be manipulated to ensure a normal distribution (or other distribution required by the ML model). In the case of this analysis, filtering and cleaning is not needed because the data is already in a form that can be visualized easily.

Finally, the data is visualized in three plots, a candlestick plot, a line plot, and a bar plot. The candlestick plot is a line plot where each data point is a bar that shows the open, close, high, and low prices. The size of the bar represents the volume of stocks traded in that day. The bars are then connected by a line and when looking at the entire time frame, trends can be seen. The line plot will be used to show the closing prices over time. The bar plot will show the overall volume of trading of each stock in the entire time frame. These three plots will be plotly objects, and thus can be manipuated and zoomed in on to see trends both globally and regionally over the time period. The candle stick will provide a convient display of all the data, while the line and bar plots break down the candle stick plot to scrutinize the data on a magnified scale.

Analysis

Discussion

The findings from data preprocessing and visualization reveals multiple insights. First, beyond cyclical fluxations, the price of every stock has increased since the initial 2010 open. Additionally, not all companies existed in 2010 and thus it can be seen that some stocks don’t appear until after 2010. Another valuable insight found was the large increase in growth of a majority of the companies in 2018. If this trend is to continue, it is beneficial to invest in the market now, though it would be wise to view other companies that have experienced exponential growth and review the times before prices settled to a lower value, the dot com boom of the early 2000s would be a good case study.

Looking at the bar plot of all companies, it is clear that Apple was the most popular stock in the dataset, this is clear from its 150% larger volume than the next most traded stock. Finally, the crash caused by the COVID-19 pandemic is clearly visible across all the companies, when in the March of 2020, stock prices plummetted.

Q2 Backwards verification: if we invested x amount of dollars in 2010, how much would it be worth in 2022, when would be a good/bad time to pull investment out of the market.

Introduction

A fundamental question for any investor is understanding how an initial investment grows over time. This analysis aims to determine the value of an investment made in 2010 in various technology stocks and its worth in 2022. To answer this, we’ll need the historical closing prices of each stock, which will help us to calculate the investment’s growth over the specified period. The second part of the analysis aims to determine good and bad times to pull an investment out of the market.

First, we’re loading up all the stock and company information from two CSV files. We then make copies of this data to work with, ensuring we don’t mess with the original files. The goal here is to gather basic statistics like the average, median, highest, lowest, and standard deviation for various aspects of the stock prices, such as the opening and closing prices. This provides us with a summary that gives a broad overview of each stock’s performance over time.

Summary Statistics:
                    open                                                 \
                    mean      median        min         max         std   
stock_symbol                                                              
AAPL           51.274171   29.745001   6.870357  182.630005   47.316509   
ADBE          186.023938   97.589996  22.969999  696.280029  173.562115   
AMZN           58.937204   36.325001   5.296500  187.199997   54.138607   
CRM           103.419948   76.290001  15.522500  310.000000   71.287894   
CSCO           33.493880   29.500000  13.930000   64.040001   12.643887   
GOOGL          49.149625   38.521000  10.968719  151.250000   35.809816   
IBM           148.427471  143.173996  90.439774  205.908218   23.998357   
INTC           36.481406   34.270000  17.879999   68.199997   12.912534   
META          147.874275  141.620002  18.080000  381.679993   86.734656   
MSFT          100.048490   55.660000  23.090000  344.619995   88.228008   
NFLX          188.242206  110.010002   6.960000  692.349976  178.967463   
NVDA           50.560335   11.902500   2.180000  335.170013   69.538684   
ORCL           46.242177   41.750000  21.459999  104.290001   16.864193   
TSLA           58.859467   16.229000   1.076000  411.470001   95.677282   

                    high                                                 ...  \
                    mean      median        min         max         std  ...   
stock_symbol                                                             ...   
AAPL           51.845876   29.980000   7.000000  182.940002   47.926721  ...   
ADBE          188.208985   98.239998  23.360001  699.539978  175.674664  ...   
AMZN           59.610805   36.500000   5.564500  188.654007   54.785319  ...   
CRM           104.720219   77.160004  15.625000  311.750000   72.149412  ...   
CSCO           33.804014   29.770000  14.120000   64.290001   12.760317  ...   
GOOGL          49.638485   38.930000  11.068068  151.546494   36.215672  ...   
IBM           149.571376  144.160004  93.441681  206.405350   24.013109  ...   
INTC           36.875197   34.580002  17.920000   69.290001   13.090012  ...   
META          149.757980  143.415000  18.270000  384.329987   87.818979  ...   
MSFT          101.039621   56.000000  23.320000  349.670013   89.181105  ...   
NFLX          191.178020  111.900002   7.178571  700.989990  181.488846  ...   
NVDA           51.504697   11.982500   2.262500  346.470001   70.979752  ...   
ORCL           46.696918   42.000000  21.680000  106.339996   17.059092  ...   
TSLA           60.174863   16.491000   1.108667  414.496674   97.873400  ...   

               adj_close                                                 \
                    mean      median        min         max         std   
stock_symbol                                                              
AAPL           49.445122   27.385101   5.846675  180.959732   47.810585   
ADBE          186.022299   97.720001  22.690001  688.369995  173.466083   
AMZN           58.905287   36.382500   5.430500  186.570496   54.085034   
CRM           103.400510   76.260002  15.520000  309.959991   71.213839   
CSCO           28.624463   23.570276   9.743538   61.521923   13.590111   
GOOGL          49.148954   38.538502  10.912663  149.838501   35.803048   
IBM           113.148579  113.695961  75.138626  150.570007   14.336266   
INTC           31.310983   28.492294  12.135988   64.383247   13.745841   
META          147.913244  142.065002  17.730000  382.179993   86.763257   
MSFT           95.285446   50.052330  17.769510  339.924835   89.253596   
NFLX          188.252178  110.099998   7.018571  691.690002  178.877130   
NVDA           50.282939   11.685297   2.037410  333.407379   69.495321   
ORCL           42.572566   37.007454  17.991089  101.501656   17.867223   
TSLA           58.805222   16.222334   1.053333  409.970001   95.544413   

                    volume                                                   
                      mean       median       min         max           std  
stock_symbol                                                                 
AAPL          2.563255e+08  166674000.0  35195900  1880998000  2.225768e+08  
ADBE          3.814337e+06    2948500.0    589200   108752400  3.598144e+06  
AMZN          8.833999e+07   74592000.0  17626000   848422000  5.309249e+07  
CRM           6.910973e+06    5548800.0   1084700    64562800  5.048860e+06  
CSCO          3.269656e+07   25482400.0   5720500   560040200  2.570963e+07  
GOOGL         6.018647e+07   41234000.0   9312000   592399008  4.957963e+07  
IBM           5.036545e+06    4345189.0   1247878    39814421  2.772073e+06  
INTC          3.607170e+07   29874600.0   5893800   199002600  2.123178e+07  
META          3.117815e+07   23239000.0   5913100   573576400  2.713267e+07  
MSFT          3.801647e+07   32280800.0   7425600   319317900  2.147328e+07  
NFLX          1.841485e+07   11961800.0   1144000   315541800  2.054316e+07  
NVDA          5.080613e+07   43395600.0   4564400   369292800  3.210953e+07  
ORCL          1.801856e+07   14699800.0   2754900   183503900  1.251053e+07  
TSLA          9.351647e+07   75914250.0   1777500   914082000  8.164780e+07  

[14 rows x 30 columns]

Next, we’re double-checking to make sure our data doesn’t have any gaps (missing values). Then, we’re doing some detective work to spot any weird or extreme data points—what we call ‘outliers’—that might throw off our analysis. We use the method Z-score to find these outliers, which helps us measure how unusual a data point is. If it’s too far from what’s normal (more than 3 standard deviations), we remove it to keep our data clean.

Null Values from Stocks dataset stock_symbol    0
date            0
open            0
high            0
low             0
close           0
adj_close       0
volume          0
dtype: int64
Null Values from companies dataset stock_symbol    0
company         0
dtype: int64

Approach

To understand how an investment has grown, we’ll look at the stock prices at the start of 2010 and compare them to the end of 2022. We’ll adjust these prices so that they all start at the same point, which makes it fair to compare different stocks. This way, we’re not distracted by some stocks being naturally more expensive than others; we’re focusing purely on how much they have grown proportionally.

We’re going to use line charts to show this growth over time. Each company’s growth curve will have its own color, making it easy to see which stocks are stars and which are not. These charts help us spot the times when stocks were soaring and when they were not doing so well.

To understand when to buy or sell a stock, we’ll use a common technique called the moving average crossover. Think of this as tracking two different running averages of a stock’s price: one that looks at the last 50 days (short-term view) and another that looks at the last 200 days (long-term view). When the short-term line crosses above the long-term line, it’s like a green light that the stock’s price might be heading up—a hint that it might be a good time to buy. When it crosses below, it’s a red flag that prices could be going down, suggesting it might be time to sell.

We’ll map these two averages onto a chart, which will help us spot exactly where these crossovers happen. It’s a straightforward method but well-regarded in the finance world for spotting when a stock’s trend might be changing direction.

Our analysis will calculate these two averages for each stock and lay them over a chart of the stock’s actual prices. We’re looking for where these average lines cross over each other—these are the critical moments that might signal to investors to act. To showcase this, we’ll use a type of chart called a candlestick chart, which is great for showing not just the average trends, but also giving us a detailed snapshot of stock price movements over time.

Analysis

Let’s assume we put $1,000 into each stock at the beginning of 2010. We’ll use our adjusted prices to track how much that $1,000 would have turned into by 2022. By looking at the charts for each stock, we can see the final tally for our investment as of 2022, giving us a clear picture of where our hypothetical $1,000 would have taken us over 12 years.

In this part, We take the list of stock symbols and create a line graph for each stock that shows how its closing price has changed day by day. The result is a colorful chart where each line represents a company’s stock, making it easy to see how stock prices have moved over time.

In this part, we’re starting all the stocks from the same line (‘normalization’), so we can compare them fairly. Imagine every stock begins at $10 in 2010, and we track how this value changes. This gives us a clear picture of each stock’s performance relative to the others, regardless of their actual price differences. We draw this out on a chart, so it’s easy to follow their growth over the years.

In this part, we’re focusing on Apple’s stock (AAPL) as an example, and we’re using two smoothed-out lines to help us decide when might be a good time to buy or sell. These lines represent the average closing price over the last 50 and 200 days. We plot these on a special kind of graph called a ‘candlestick chart’, which not only shows the average trends but also the daily price movements in more detail. This chart helps us spot those key moments where the short-term average crosses over the long-term average, which can signal whether it’s potentially a good time to get in or out of the market.

Discussion

Insights into investment strategies and market dynamics.

By leveraging historical market data and DMA models, we gained a understanding of how different investment options perform over time. This allowed us to identify trends and patterns that could inform investment decisions, such as identifying stocks with consistent growth or bonds with stable returns. There are a few perceptions on the visualizations. In 2010, IBM stood out with the highest close price among the companies analyzed. From 2018 to 2022, there was a notable exponential growth in stock close prices. By 2021, both Netflix and ADE were nearly on par with their close prices. However, they both experienced declines afterward. Apart from Tesla and Netflix, the other companies remained relatively stable in their normalized stock market performance. Netflix maintained stability in its stock market performance.

Market conditions are subject to change, and unforeseen events or economic factors can impact investment performance. Therefore, it’s essential for investors to continuously monitor and adapt their strategies in response to evolving market dynamics. Analyzing daily price data using the 200-day moving average (DMA) and the 50-day DMA method revealed that the 50 DMA consistently showed stronger pricing performance compared to the 200 DMA.